This study will:
- Create a model to forecast key growth stages of canola
- A sclerotinia stem rot (SSR) risk model to help producers with fungicide treatment decisions and other agronomic activities
- A yield model to forecast canola production at local and regional levels
The major objectives of this project were to develop and deploy forecasting tools for canola growth stages, sclerotinia stem rot risk, and canola yield. There is a strong association between Sclerotinia stem rot (SSR) incidence and weather conditions during and close to the canola flowering period. The success of developing a sclerotinia stem rot forecasting system relies on the ability to precisely predict the canola flowering stage. In addition, there is a linkage between growth stages and insect damage on crops. Although studies have been conducted to understand the relationships between weather variables and canola growth and development as well as SSR incidence, precise models to predict growth stages and SSR are not available to canola growers in western Canada. Agriculture and Agri-Food Canada developed a yield forecasting model for canola at the regional level. The integration of a growth stage prediction tool will help to refine the yield forecasting model.
The results of the study led to the development of a ‘web-based’ tool using an environmental-plant phenotype model that provides producers with decision support to apply or forego fungicide application. This is important for grower profitability, reduced production risk, and environmental sustainability. It validates and builds upon the Canola Council of Canada “Sclerotinia Checklist” for fungicide application timing. Also, the physiological days (P-days) to predict the growth stages of a developing canola crop showed to be more consistent than the GDD (growing degree days). Having a specific model like this to follow for fungicide timing will allow producers to apply fungicide (if needed) at the best possible time, especially if combined with spore levels in a specific field.